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Researchers use AI to analyze tweets debating vaccination and climate change

#artificialintelligence

Using artificial intelligence (AI) researchers have found that between 2007 and 2016 online sentiments around climate change were uniform, but this was not the case with vaccination. Climate change and vaccinations might share many of the same social and environmental elements, but that doesn't mean the debates are divided along the same demographics. A research team from the University of Waterloo and the University of Guelph trained a machine-learning algorithm to analyze a massive number of tweets about climate change and vaccination. The researchers found that climate change sentiment was overwhelmingly on the pro side of those that believe climate change is because of human activity and requires action. There was also a significant amount of interaction between users with opposite sentiments about climate change.


Researchers use AI to analyze tweets debating vaccination and climate change

#artificialintelligence

Using artificial intelligence (AI) researchers have found that between 2007 and 2016 online sentiments around climate change were uniform, but this was not the case with vaccination. Climate change and vaccinations might share many of the same social and environmental elements, but that doesn't mean the debates are divided along the same demographics. A research team from the University of Waterloo and the University of Guelph trained a machine-learning algorithm to analyze a massive number of tweets about climate change and vaccination. The researchers found that climate change sentiment was overwhelmingly on the pro side of those that believe climate change is because of human activity and requires action. There was also a significant amount of interaction between users with opposite sentiments about climate change.


Donald Trump Is Still a Public Health Threat

Mother Jones

Imagine being in a position to save tens of thousands of lives--perhaps hundreds of thousands--and not doing so? That's one of the core questions that remain in the wake of the Trump presidency. The guy in charge could have prevented the deaths of so many Americans just by advocating and reaffirming basic public health guidances, such as mask-wearing, and he chose not to. Last month, the Lancet Commission on Public Policy and Health in the Trump Era declared that about 40 percent of the nation's COVID-19 deaths could have been averted had the United States taken these fundamental steps, and it offered a damning verdict: Donald Trump's "inept and insufficient" response to the coronavirus crisis was partly to blame. That is, while Trump was in office, his actions--or inactions--killed many Americans.


Twitter discussions and emotions about COVID-19 pandemic: a machine learning approach

arXiv.org Machine Learning

The objective of the study is to examine coronavirus disease (COVID-19) related discussions, concerns, and sentiments that emerged from tweets posted by Twitter users. We analyze 4 million Twitter messages related to the COVID-19 pandemic using a list of 25 hashtags such as "coronavirus," "COVID-19," "quarantine" from March 1 to April 21 in 2020. We use a machine learning approach, Latent Dirichlet Allocation (LDA), to identify popular unigram, bigrams, salient topics and themes, and sentiments in the collected Tweets. Popular unigrams include "virus," "lockdown," and "quarantine." Popular bigrams include "COVID-19," "stay home," "corona virus," "social distancing," and "new cases." We identify 13 discussion topics and categorize them into five different themes, such as "public health measures to slow the spread of COVID-19," "social stigma associated with COVID-19," "coronavirus news cases and deaths," "COVID-19 in the United States," and "coronavirus cases in the rest of the world". Across all identified topics, the dominant sentiments for the spread of coronavirus are anticipation that measures that can be taken, followed by a mixed feeling of trust, anger, and fear for different topics. The public reveals a significant feeling of fear when they discuss the coronavirus new cases and deaths than other topics. The study shows that Twitter data and machine learning approaches can be leveraged for infodemiology study by studying the evolving public discussions and sentiments during the COVID-19. Real-time monitoring and assessment of the Twitter discussion and concerns can be promising for public health emergency responses and planning. Already emerged pandemic fear, stigma, and mental health concerns may continue to influence public trust when there occurs a second wave of COVID-19 or a new surge of the imminent pandemic.


Examining Patterns of Influenza Vaccination in Social Media

AAAI Conferences

Traditional data on influenza vaccination has several limitations: high cost, limited coverage of underrepresented groups, and low sensitivity to emerging public health issues. Social media, such as Twitter, provide an alternative way to understand a population’s vaccination-related opinions and behaviors. In this study, we build and employ several natural language classifiers to examine and analyze behavioral patterns regarding influenza vaccination in Twitter across three dimensions: temporality (by week and month), geography (by US region), and demography (by gender). Our best results are highly correlated official government data, with a correlation over 0.90, providing validation of our approach. We then suggest a number of directions for future work.